A Computing Model: The Closed-Loop Optimal Control for Large-Scale One-of-a-Kind Production Based on Multilevel Hierarchical PERT-Petri Net
Bibliographic record
Abstract
For the design and operation of interim product manufacturing systems, large-scale one-of-a-kind production (OKP) features disruptions and lack of synchronization, which challenges real-time production process systematic control. The difficulty lies in two aspects. First, large-scale OKP is generally characterized by product design uncertainty and changes in resource availability, which often causes varied working time with workforce allocation variations. Second, specific types of large-scale OKP provides discrete production with complicated structures. There is the complicated work breakdown structure (WBS) of top-down and simple-to-complex refinement. Moreover, the production task relation structure in WBS, which consists of tandem structure, parallel structure and double-level-nested parallel structure, is complex. Indeed, many large-scale OKP enterprises do not have an effective method to both control and plan adjustment in a systematic way for dealing with the progress of OKP operations under workforce allocation disruptions and different working time scenarios. The traditional production project management and control system, theory, and methods do not handle this situation well since these technologies are developed with a view to managing production periods instead of controlling time-related cost systematically during production. Regarding the interim product production process in large-scale OKP as a manufacturing system, in this article we propose a cost dynamic control and optimization method based on the multilevel hierarchical PERT-Petri net (MLHPP) to achieve a closed-loop production dynamic control structure in large-scale OKP. The proposed modeling method and algorithms, through an industrial implementation in shipbuilding interim production (e.g., a ship block building), demonstrate a computing model for structuring and controlling large-scale OKP systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".